Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import os
import helper

if not os.path.exists(os.path.join(data_dir, 'mnist')):
    helper.download_extract('mnist', data_dir)
if not os.path.exists(os.path.join(data_dir, 'celeba')):
    helper.download_extract('celeba', data_dir)
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f13419eba90>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f13418cdac8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_real = tf.placeholder(
        tf.float32,
        [ None, image_width, image_height, image_channels ],
        name='input_real'
    )
    inputs_z = tf.placeholder(tf.float32, [ None, z_dim ], name='input_z')
    lr = tf.placeholder(tf.float32, name='learning_rate')

    return inputs_real, inputs_z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [64]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        alpha = 0.2
        # Hidden layer
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 28x28x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 14x14x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 7x7x256

        # Flatten it
        n_units=7 * 7 * 256
        flat = tf.reshape(relu3, [-1, n_units])
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [73]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.2
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7 * 7 * 512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x3 now
        out = tf.tanh(logits)

        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    smooth = 0.1
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1.0 - smooth)
        )
    )
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake))
    )
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake))
    )

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [32]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [ var for var in t_vars if var.name.startswith('discriminator') ]
    g_vars = [ var for var in t_vars if var.name.startswith('generator') ]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [76]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    n_images = 25
    data_cnt, image_width, image_height, image_channels = data_shape

    tf.reset_default_graph()
    input_real, input_z, input_rate = \
        model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, input_rate, beta1)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps += 1
                
                # Normalize to be between -1 and 1
                batch_images *= 2.0

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={ input_real: batch_images, input_z: batch_z, input_rate: learning_rate })
                _ = sess.run(g_train_opt, feed_dict={ input_real: batch_images, input_z: batch_z, input_rate: learning_rate })

                if steps % 10 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({ input_z: batch_z, input_real: batch_images })
                    train_loss_g = g_loss.eval({ input_z: batch_z })

                    print(
                        'Epoch {}/{} {}...'.format(epoch_i + 1, epoch_count, steps),
                        'Discriminator Loss: {:.4f}...'.format(train_loss_d),
                        'Generator Loss: {:.4f}'.format(train_loss_g)
                    )
                    show_generator_output(sess, n_images, input_z, image_channels, data_image_mode)

            # At the end of each epoch, get the losses and print them out
            train_loss_d = sess.run(d_loss, { input_z: batch_z, input_real: batch_images })
            train_loss_g = g_loss.eval({ input_z: batch_z })

            print(
                'Epoch {}/{}...'.format(epoch_i + 1, epochs),
                'Discriminator Loss: {:.4f}...'.format(train_loss_d),
                'Generator Loss: {:.4f}'.format(train_loss_g)
            )
            show_generator_output(sess, n_images, input_z, image_channels, data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [77]:
batch_size = 49 # 100
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
# with tf.Graph().as_default():
train(
    epochs, 
    batch_size, 
    z_dim, 
    learning_rate, 
    beta1, 
    mnist_dataset.get_batches,
    mnist_dataset.shape, 
    mnist_dataset.image_mode
)
Epoch 1/2 10... Discriminator Loss: 1.1056... Generator Loss: 0.6999
Epoch 1/2 20... Discriminator Loss: 0.8488... Generator Loss: 1.0378
Epoch 1/2 30... Discriminator Loss: 0.9092... Generator Loss: 1.0363
Epoch 1/2 40... Discriminator Loss: 2.5400... Generator Loss: 0.1447
Epoch 1/2 50... Discriminator Loss: 1.1287... Generator Loss: 1.5502
Epoch 1/2 60... Discriminator Loss: 1.5880... Generator Loss: 0.4094
Epoch 1/2 70... Discriminator Loss: 0.4530... Generator Loss: 2.4014
Epoch 1/2 80... Discriminator Loss: 1.9564... Generator Loss: 0.3207
Epoch 1/2 90... Discriminator Loss: 1.7058... Generator Loss: 0.3397
Epoch 1/2 100... Discriminator Loss: 1.4621... Generator Loss: 0.4983
Epoch 1/2 110... Discriminator Loss: 2.4557... Generator Loss: 0.1481
Epoch 1/2 120... Discriminator Loss: 1.2124... Generator Loss: 3.8767
Epoch 1/2 130... Discriminator Loss: 1.2977... Generator Loss: 0.5885
Epoch 1/2 140... Discriminator Loss: 1.6557... Generator Loss: 0.3339
Epoch 1/2 150... Discriminator Loss: 1.1383... Generator Loss: 1.3759
Epoch 1/2 160... Discriminator Loss: 1.4808... Generator Loss: 0.4459
Epoch 1/2 170... Discriminator Loss: 1.1728... Generator Loss: 0.7800
Epoch 1/2 180... Discriminator Loss: 1.0899... Generator Loss: 0.8510
Epoch 1/2 190... Discriminator Loss: 1.1757... Generator Loss: 1.0201
Epoch 1/2 200... Discriminator Loss: 1.3863... Generator Loss: 1.4115
Epoch 1/2 210... Discriminator Loss: 1.4085... Generator Loss: 1.8232
Epoch 1/2 220... Discriminator Loss: 1.1231... Generator Loss: 0.8885
Epoch 1/2 230... Discriminator Loss: 1.3767... Generator Loss: 0.5917
Epoch 1/2 240... Discriminator Loss: 1.3118... Generator Loss: 1.4349
Epoch 1/2 250... Discriminator Loss: 1.7185... Generator Loss: 1.4715
Epoch 1/2 260... Discriminator Loss: 1.2800... Generator Loss: 0.7093
Epoch 1/2 270... Discriminator Loss: 1.4755... Generator Loss: 1.5874
Epoch 1/2 280... Discriminator Loss: 1.2463... Generator Loss: 0.5825
Epoch 1/2 290... Discriminator Loss: 1.2014... Generator Loss: 0.7098
Epoch 1/2 300... Discriminator Loss: 1.2105... Generator Loss: 0.6218
Epoch 1/2 310... Discriminator Loss: 0.9761... Generator Loss: 1.4991
Epoch 1/2 320... Discriminator Loss: 1.0403... Generator Loss: 0.8616
Epoch 1/2 330... Discriminator Loss: 1.1702... Generator Loss: 0.6562
Epoch 1/2 340... Discriminator Loss: 1.1170... Generator Loss: 1.1532
Epoch 1/2 350... Discriminator Loss: 1.0535... Generator Loss: 0.8787
Epoch 1/2 360... Discriminator Loss: 1.0536... Generator Loss: 1.2203
Epoch 1/2 370... Discriminator Loss: 1.2253... Generator Loss: 0.6644
Epoch 1/2 380... Discriminator Loss: 1.2030... Generator Loss: 1.3126
Epoch 1/2 390... Discriminator Loss: 1.2366... Generator Loss: 0.8825
Epoch 1/2 400... Discriminator Loss: 1.2240... Generator Loss: 0.6225
Epoch 1/2 410... Discriminator Loss: 1.2695... Generator Loss: 0.5885
Epoch 1/2 420... Discriminator Loss: 1.1747... Generator Loss: 1.5951
Epoch 1/2 430... Discriminator Loss: 1.5276... Generator Loss: 0.3945
Epoch 1/2 440... Discriminator Loss: 1.5970... Generator Loss: 0.3857
Epoch 1/2 450... Discriminator Loss: 1.3486... Generator Loss: 1.3965
Epoch 1/2 460... Discriminator Loss: 1.0962... Generator Loss: 1.0120
Epoch 1/2 470... Discriminator Loss: 1.1050... Generator Loss: 1.1752
Epoch 1/2 480... Discriminator Loss: 1.1003... Generator Loss: 0.9479
Epoch 1/2 490... Discriminator Loss: 1.3359... Generator Loss: 0.7323
Epoch 1/2 500... Discriminator Loss: 1.2407... Generator Loss: 1.5251
Epoch 1/2 510... Discriminator Loss: 1.3256... Generator Loss: 0.4883
Epoch 1/2 520... Discriminator Loss: 1.3124... Generator Loss: 0.5647
Epoch 1/2 530... Discriminator Loss: 1.0700... Generator Loss: 1.3323
Epoch 1/2 540... Discriminator Loss: 1.0552... Generator Loss: 1.0705
Epoch 1/2 550... Discriminator Loss: 1.3097... Generator Loss: 0.7415
Epoch 1/2 560... Discriminator Loss: 1.0096... Generator Loss: 1.0290
Epoch 1/2 570... Discriminator Loss: 1.0380... Generator Loss: 0.7973
Epoch 1/2 580... Discriminator Loss: 1.8131... Generator Loss: 0.2742
Epoch 1/2 590... Discriminator Loss: 0.9314... Generator Loss: 1.1082
Epoch 1/2 600... Discriminator Loss: 1.1625... Generator Loss: 0.6595
Epoch 1/2 610... Discriminator Loss: 1.4976... Generator Loss: 0.3974
Epoch 1/2 620... Discriminator Loss: 1.1002... Generator Loss: 0.9109
Epoch 1/2 630... Discriminator Loss: 1.1319... Generator Loss: 1.4028
Epoch 1/2 640... Discriminator Loss: 1.3728... Generator Loss: 0.4530
Epoch 1/2 650... Discriminator Loss: 1.3752... Generator Loss: 0.4552
Epoch 1/2 660... Discriminator Loss: 1.0521... Generator Loss: 0.9030
Epoch 1/2 670... Discriminator Loss: 0.9268... Generator Loss: 1.7537
Epoch 1/2 680... Discriminator Loss: 1.2238... Generator Loss: 1.6618
Epoch 1/2 690... Discriminator Loss: 1.1728... Generator Loss: 1.4187
Epoch 1/2 700... Discriminator Loss: 1.2556... Generator Loss: 0.6678
Epoch 1/2 710... Discriminator Loss: 2.0121... Generator Loss: 0.2342
Epoch 1/2 720... Discriminator Loss: 1.0410... Generator Loss: 0.8757
Epoch 1/2 730... Discriminator Loss: 1.3232... Generator Loss: 1.5756
Epoch 1/2 740... Discriminator Loss: 0.9325... Generator Loss: 1.0592
Epoch 1/2 750... Discriminator Loss: 1.0259... Generator Loss: 1.2558
Epoch 1/2 760... Discriminator Loss: 0.8447... Generator Loss: 1.0878
Epoch 1/2 770... Discriminator Loss: 1.3835... Generator Loss: 0.4809
Epoch 1/2 780... Discriminator Loss: 0.9764... Generator Loss: 0.9295
Epoch 1/2 790... Discriminator Loss: 1.0587... Generator Loss: 1.4190
Epoch 1/2 800... Discriminator Loss: 0.9857... Generator Loss: 1.0097
Epoch 1/2 810... Discriminator Loss: 0.9394... Generator Loss: 1.7001
Epoch 1/2 820... Discriminator Loss: 0.9805... Generator Loss: 1.3083
Epoch 1/2 830... Discriminator Loss: 0.9804... Generator Loss: 1.2263
Epoch 1/2 840... Discriminator Loss: 1.0736... Generator Loss: 0.7573
Epoch 1/2 850... Discriminator Loss: 0.9641... Generator Loss: 0.8832
Epoch 1/2 860... Discriminator Loss: 1.1202... Generator Loss: 1.4072
Epoch 1/2 870... Discriminator Loss: 0.8160... Generator Loss: 1.7590
Epoch 1/2 880... Discriminator Loss: 1.2419... Generator Loss: 0.7572
Epoch 1/2 890... Discriminator Loss: 1.8984... Generator Loss: 2.7753
Epoch 1/2 900... Discriminator Loss: 0.9263... Generator Loss: 1.2917
Epoch 1/2 910... Discriminator Loss: 1.0337... Generator Loss: 0.8923
Epoch 1/2 920... Discriminator Loss: 1.1822... Generator Loss: 0.6569
Epoch 1/2 930... Discriminator Loss: 0.9907... Generator Loss: 1.6062
Epoch 1/2 940... Discriminator Loss: 1.5845... Generator Loss: 0.3914
Epoch 1/2 950... Discriminator Loss: 0.8070... Generator Loss: 1.1743
Epoch 1/2 960... Discriminator Loss: 1.6891... Generator Loss: 2.0162
Epoch 1/2 970... Discriminator Loss: 1.2067... Generator Loss: 0.7046
Epoch 1/2 980... Discriminator Loss: 0.8663... Generator Loss: 1.9123
Epoch 1/2 990... Discriminator Loss: 1.2037... Generator Loss: 0.6225
Epoch 1/2 1000... Discriminator Loss: 0.8733... Generator Loss: 1.0494
Epoch 1/2 1010... Discriminator Loss: 0.9433... Generator Loss: 0.8792
Epoch 1/2 1020... Discriminator Loss: 0.8696... Generator Loss: 1.4478
Epoch 1/2 1030... Discriminator Loss: 0.9526... Generator Loss: 0.9797
Epoch 1/2 1040... Discriminator Loss: 0.9220... Generator Loss: 1.1018
Epoch 1/2 1050... Discriminator Loss: 1.2291... Generator Loss: 0.6250
Epoch 1/2 1060... Discriminator Loss: 1.3462... Generator Loss: 0.7056
Epoch 1/2 1070... Discriminator Loss: 1.6228... Generator Loss: 0.4145
Epoch 1/2 1080... Discriminator Loss: 0.9043... Generator Loss: 1.0801
Epoch 1/2 1090... Discriminator Loss: 1.1656... Generator Loss: 0.7260
Epoch 1/2 1100... Discriminator Loss: 1.5524... Generator Loss: 0.4994
Epoch 1/2 1110... Discriminator Loss: 0.8585... Generator Loss: 1.0597
Epoch 1/2 1120... Discriminator Loss: 1.2045... Generator Loss: 0.6366
Epoch 1/2 1130... Discriminator Loss: 1.4495... Generator Loss: 0.5598
Epoch 1/2 1140... Discriminator Loss: 0.8821... Generator Loss: 1.0059
Epoch 1/2 1150... Discriminator Loss: 1.1904... Generator Loss: 0.6403
Epoch 1/2 1160... Discriminator Loss: 0.9527... Generator Loss: 1.1095
Epoch 1/2 1170... Discriminator Loss: 1.0489... Generator Loss: 0.8809
Epoch 1/2 1180... Discriminator Loss: 1.4942... Generator Loss: 0.4390
Epoch 1/2 1190... Discriminator Loss: 1.2938... Generator Loss: 0.6660
Epoch 1/2 1200... Discriminator Loss: 1.0162... Generator Loss: 1.0764
Epoch 1/2 1210... Discriminator Loss: 1.5501... Generator Loss: 0.4152
Epoch 1/2 1220... Discriminator Loss: 1.1078... Generator Loss: 0.7586
Epoch 1/2... Discriminator Loss: 1.0354... Generator Loss: 0.8908
Epoch 2/2 10... Discriminator Loss: 1.1190... Generator Loss: 0.8871
Epoch 2/2 20... Discriminator Loss: 0.8851... Generator Loss: 1.0730
Epoch 2/2 30... Discriminator Loss: 1.6777... Generator Loss: 0.3962
Epoch 2/2 40... Discriminator Loss: 1.2339... Generator Loss: 0.5793
Epoch 2/2 50... Discriminator Loss: 0.9194... Generator Loss: 1.0144
Epoch 2/2 60... Discriminator Loss: 0.9397... Generator Loss: 1.0312
Epoch 2/2 70... Discriminator Loss: 0.9513... Generator Loss: 1.1853
Epoch 2/2 80... Discriminator Loss: 0.9970... Generator Loss: 0.9477
Epoch 2/2 90... Discriminator Loss: 1.1328... Generator Loss: 0.6416
Epoch 2/2 100... Discriminator Loss: 1.1754... Generator Loss: 0.6947
Epoch 2/2 110... Discriminator Loss: 0.7628... Generator Loss: 1.4163
Epoch 2/2 120... Discriminator Loss: 1.4791... Generator Loss: 1.7286
Epoch 2/2 130... Discriminator Loss: 0.7026... Generator Loss: 2.0249
Epoch 2/2 140... Discriminator Loss: 0.9378... Generator Loss: 0.9693
Epoch 2/2 150... Discriminator Loss: 0.8434... Generator Loss: 1.7409
Epoch 2/2 160... Discriminator Loss: 1.3751... Generator Loss: 2.6578
Epoch 2/2 170... Discriminator Loss: 0.7300... Generator Loss: 1.4828
Epoch 2/2 180... Discriminator Loss: 0.9691... Generator Loss: 1.1158
Epoch 2/2 190... Discriminator Loss: 1.7267... Generator Loss: 0.3717
Epoch 2/2 200... Discriminator Loss: 1.2338... Generator Loss: 0.7561
Epoch 2/2 210... Discriminator Loss: 0.7917... Generator Loss: 1.3121
Epoch 2/2 220... Discriminator Loss: 1.7370... Generator Loss: 0.3667
Epoch 2/2 230... Discriminator Loss: 0.9183... Generator Loss: 1.2099
Epoch 2/2 240... Discriminator Loss: 0.6603... Generator Loss: 1.4637
Epoch 2/2 250... Discriminator Loss: 1.1965... Generator Loss: 0.7193
Epoch 2/2 260... Discriminator Loss: 1.0473... Generator Loss: 0.8678
Epoch 2/2 270... Discriminator Loss: 0.8575... Generator Loss: 1.3541
Epoch 2/2 280... Discriminator Loss: 0.7261... Generator Loss: 1.3140
Epoch 2/2 290... Discriminator Loss: 0.9181... Generator Loss: 0.9905
Epoch 2/2 300... Discriminator Loss: 1.2960... Generator Loss: 0.5858
Epoch 2/2 310... Discriminator Loss: 0.7296... Generator Loss: 1.4693
Epoch 2/2 320... Discriminator Loss: 1.9203... Generator Loss: 0.3249
Epoch 2/2 330... Discriminator Loss: 1.1742... Generator Loss: 1.3635
Epoch 2/2 340... Discriminator Loss: 0.8058... Generator Loss: 1.1252
Epoch 2/2 350... Discriminator Loss: 0.9315... Generator Loss: 1.4434
Epoch 2/2 360... Discriminator Loss: 0.9797... Generator Loss: 0.8889
Epoch 2/2 370... Discriminator Loss: 1.1111... Generator Loss: 0.7862
Epoch 2/2 380... Discriminator Loss: 0.9166... Generator Loss: 0.9214
Epoch 2/2 390... Discriminator Loss: 0.5763... Generator Loss: 2.2844
Epoch 2/2 400... Discriminator Loss: 0.8096... Generator Loss: 2.5262
Epoch 2/2 410... Discriminator Loss: 0.9488... Generator Loss: 1.3313
Epoch 2/2 420... Discriminator Loss: 0.8015... Generator Loss: 1.3924
Epoch 2/2 430... Discriminator Loss: 0.6263... Generator Loss: 1.6748
Epoch 2/2 440... Discriminator Loss: 0.7481... Generator Loss: 1.8820
Epoch 2/2 450... Discriminator Loss: 1.4241... Generator Loss: 0.5354
Epoch 2/2 460... Discriminator Loss: 2.4713... Generator Loss: 0.1959
Epoch 2/2 470... Discriminator Loss: 1.1475... Generator Loss: 0.9966
Epoch 2/2 480... Discriminator Loss: 1.0044... Generator Loss: 2.3914
Epoch 2/2 490... Discriminator Loss: 0.9268... Generator Loss: 1.1228
Epoch 2/2 500... Discriminator Loss: 0.7029... Generator Loss: 1.4812
Epoch 2/2 510... Discriminator Loss: 1.1710... Generator Loss: 0.7188
Epoch 2/2 520... Discriminator Loss: 0.6298... Generator Loss: 1.7562
Epoch 2/2 530... Discriminator Loss: 0.6720... Generator Loss: 2.1133
Epoch 2/2 540... Discriminator Loss: 1.5295... Generator Loss: 0.5255
Epoch 2/2 550... Discriminator Loss: 0.9266... Generator Loss: 1.0145
Epoch 2/2 560... Discriminator Loss: 0.7427... Generator Loss: 1.3560
Epoch 2/2 570... Discriminator Loss: 0.9813... Generator Loss: 1.0663
Epoch 2/2 580... Discriminator Loss: 1.2860... Generator Loss: 0.6756
Epoch 2/2 590... Discriminator Loss: 0.9746... Generator Loss: 0.9645
Epoch 2/2 600... Discriminator Loss: 1.3298... Generator Loss: 0.5456
Epoch 2/2 610... Discriminator Loss: 0.8911... Generator Loss: 2.6065
Epoch 2/2 620... Discriminator Loss: 0.9948... Generator Loss: 0.9712
Epoch 2/2 630... Discriminator Loss: 0.7435... Generator Loss: 2.4793
Epoch 2/2 640... Discriminator Loss: 0.7847... Generator Loss: 1.1242
Epoch 2/2 650... Discriminator Loss: 0.8209... Generator Loss: 1.3479
Epoch 2/2 660... Discriminator Loss: 0.6718... Generator Loss: 1.4671
Epoch 2/2 670... Discriminator Loss: 1.3090... Generator Loss: 0.6772
Epoch 2/2 680... Discriminator Loss: 0.9682... Generator Loss: 0.9809
Epoch 2/2 690... Discriminator Loss: 1.4820... Generator Loss: 0.5677
Epoch 2/2 700... Discriminator Loss: 1.2377... Generator Loss: 0.6740
Epoch 2/2 710... Discriminator Loss: 1.1521... Generator Loss: 0.7985
Epoch 2/2 720... Discriminator Loss: 0.7586... Generator Loss: 1.9188
Epoch 2/2 730... Discriminator Loss: 0.8845... Generator Loss: 1.1344
Epoch 2/2 740... Discriminator Loss: 0.6908... Generator Loss: 1.9615
Epoch 2/2 750... Discriminator Loss: 0.7047... Generator Loss: 1.6577
Epoch 2/2 760... Discriminator Loss: 1.0266... Generator Loss: 0.8477
Epoch 2/2 770... Discriminator Loss: 0.9753... Generator Loss: 1.3932
Epoch 2/2 780... Discriminator Loss: 0.5695... Generator Loss: 2.0383
Epoch 2/2 790... Discriminator Loss: 0.9597... Generator Loss: 1.9765
Epoch 2/2 800... Discriminator Loss: 0.8231... Generator Loss: 1.2236
Epoch 2/2 810... Discriminator Loss: 0.6509... Generator Loss: 1.5202
Epoch 2/2 820... Discriminator Loss: 0.7896... Generator Loss: 1.3401
Epoch 2/2 830... Discriminator Loss: 0.8693... Generator Loss: 1.2952
Epoch 2/2 840... Discriminator Loss: 1.6254... Generator Loss: 0.5121
Epoch 2/2 850... Discriminator Loss: 1.6335... Generator Loss: 1.3456
Epoch 2/2 860... Discriminator Loss: 0.9098... Generator Loss: 1.1635
Epoch 2/2 870... Discriminator Loss: 1.8995... Generator Loss: 0.3088
Epoch 2/2 880... Discriminator Loss: 1.6285... Generator Loss: 0.4519
Epoch 2/2 890... Discriminator Loss: 0.7995... Generator Loss: 1.8681
Epoch 2/2 900... Discriminator Loss: 1.2523... Generator Loss: 0.6430
Epoch 2/2 910... Discriminator Loss: 0.9255... Generator Loss: 0.9966
Epoch 2/2 920... Discriminator Loss: 0.9500... Generator Loss: 1.1816
Epoch 2/2 930... Discriminator Loss: 0.6401... Generator Loss: 1.7544
Epoch 2/2 940... Discriminator Loss: 0.6714... Generator Loss: 1.8890
Epoch 2/2 950... Discriminator Loss: 0.5205... Generator Loss: 1.9796
Epoch 2/2 960... Discriminator Loss: 3.0261... Generator Loss: 4.7344
Epoch 2/2 970... Discriminator Loss: 0.8373... Generator Loss: 1.5355
Epoch 2/2 980... Discriminator Loss: 0.9351... Generator Loss: 1.2201
Epoch 2/2 990... Discriminator Loss: 1.8939... Generator Loss: 0.3921
Epoch 2/2 1000... Discriminator Loss: 0.9402... Generator Loss: 2.2260
Epoch 2/2 1010... Discriminator Loss: 1.2070... Generator Loss: 0.7788
Epoch 2/2 1020... Discriminator Loss: 0.8188... Generator Loss: 1.2384
Epoch 2/2 1030... Discriminator Loss: 0.8925... Generator Loss: 1.1291
Epoch 2/2 1040... Discriminator Loss: 1.1298... Generator Loss: 0.6869
Epoch 2/2 1050... Discriminator Loss: 0.9726... Generator Loss: 0.9779
Epoch 2/2 1060... Discriminator Loss: 0.8610... Generator Loss: 1.2095
Epoch 2/2 1070... Discriminator Loss: 1.0724... Generator Loss: 0.7892
Epoch 2/2 1080... Discriminator Loss: 0.8448... Generator Loss: 1.3827
Epoch 2/2 1090... Discriminator Loss: 1.0433... Generator Loss: 0.8464
Epoch 2/2 1100... Discriminator Loss: 0.7664... Generator Loss: 1.2198
Epoch 2/2 1110... Discriminator Loss: 0.9479... Generator Loss: 2.8124
Epoch 2/2 1120... Discriminator Loss: 1.1821... Generator Loss: 0.8910
Epoch 2/2 1130... Discriminator Loss: 0.8662... Generator Loss: 1.0796
Epoch 2/2 1140... Discriminator Loss: 0.7578... Generator Loss: 1.6610
Epoch 2/2 1150... Discriminator Loss: 1.8745... Generator Loss: 0.3353
Epoch 2/2 1160... Discriminator Loss: 0.7973... Generator Loss: 1.9403
Epoch 2/2 1170... Discriminator Loss: 0.5907... Generator Loss: 1.8729
Epoch 2/2 1180... Discriminator Loss: 1.9837... Generator Loss: 0.3903
Epoch 2/2 1190... Discriminator Loss: 0.8218... Generator Loss: 1.1836
Epoch 2/2 1200... Discriminator Loss: 0.8586... Generator Loss: 1.0467
Epoch 2/2 1210... Discriminator Loss: 1.1220... Generator Loss: 1.3115
Epoch 2/2 1220... Discriminator Loss: 1.7466... Generator Loss: 0.4337
Epoch 2/2... Discriminator Loss: 1.1955... Generator Loss: 0.7630

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [78]:
batch_size = 49
z_dim = 100
learning_rate = 0.002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
# with tf.Graph().as_default():
train(
    epochs, 
    batch_size, 
    z_dim, 
    learning_rate, 
    beta1, 
    celeba_dataset.get_batches,
    celeba_dataset.shape, 
    celeba_dataset.image_mode
)
Epoch 1/1 10... Discriminator Loss: 0.9627... Generator Loss: 0.9363
Epoch 1/1 20... Discriminator Loss: 0.7809... Generator Loss: 1.2700
Epoch 1/1 30... Discriminator Loss: 0.7255... Generator Loss: 1.8411
Epoch 1/1 40... Discriminator Loss: 1.7684... Generator Loss: 0.8132
Epoch 1/1 50... Discriminator Loss: 1.9343... Generator Loss: 2.4994
Epoch 1/1 60... Discriminator Loss: 0.6292... Generator Loss: 2.0949
Epoch 1/1 70... Discriminator Loss: 0.7001... Generator Loss: 2.8725
Epoch 1/1 80... Discriminator Loss: 0.9797... Generator Loss: 1.8114
Epoch 1/1 90... Discriminator Loss: 0.9074... Generator Loss: 1.2168
Epoch 1/1 100... Discriminator Loss: 0.9263... Generator Loss: 2.1627
Epoch 1/1 110... Discriminator Loss: 1.4816... Generator Loss: 2.6132
Epoch 1/1 120... Discriminator Loss: 1.0591... Generator Loss: 1.7267
Epoch 1/1 130... Discriminator Loss: 1.7559... Generator Loss: 1.8740
Epoch 1/1 140... Discriminator Loss: 1.8137... Generator Loss: 3.9837
Epoch 1/1 150... Discriminator Loss: 0.4679... Generator Loss: 3.1175
Epoch 1/1 160... Discriminator Loss: 1.2676... Generator Loss: 0.6982
Epoch 1/1 170... Discriminator Loss: 0.7548... Generator Loss: 2.1580
Epoch 1/1 180... Discriminator Loss: 0.5396... Generator Loss: 2.3405
Epoch 1/1 190... Discriminator Loss: 2.0543... Generator Loss: 4.1234
Epoch 1/1 200... Discriminator Loss: 1.5586... Generator Loss: 5.1626
Epoch 1/1 210... Discriminator Loss: 1.1657... Generator Loss: 2.6632
Epoch 1/1 220... Discriminator Loss: 0.8038... Generator Loss: 1.3786
Epoch 1/1 230... Discriminator Loss: 1.6708... Generator Loss: 5.4221
Epoch 1/1 240... Discriminator Loss: 0.4080... Generator Loss: 3.5810
Epoch 1/1 250... Discriminator Loss: 2.7539... Generator Loss: 0.1483
Epoch 1/1 260... Discriminator Loss: 0.7248... Generator Loss: 2.3564
Epoch 1/1 270... Discriminator Loss: 0.4726... Generator Loss: 3.3928
Epoch 1/1 280... Discriminator Loss: 2.3278... Generator Loss: 0.2679
Epoch 1/1 290... Discriminator Loss: 1.7635... Generator Loss: 0.3351
Epoch 1/1 300... Discriminator Loss: 2.0961... Generator Loss: 0.2073
Epoch 1/1 310... Discriminator Loss: 1.1730... Generator Loss: 1.5591
Epoch 1/1 320... Discriminator Loss: 1.5381... Generator Loss: 0.8442
Epoch 1/1 330... Discriminator Loss: 1.8064... Generator Loss: 0.8372
Epoch 1/1 340... Discriminator Loss: 0.9921... Generator Loss: 0.9619
Epoch 1/1 350... Discriminator Loss: 1.0134... Generator Loss: 1.0698
Epoch 1/1 360... Discriminator Loss: 2.4591... Generator Loss: 0.1959
Epoch 1/1 370... Discriminator Loss: 0.9742... Generator Loss: 1.7127
Epoch 1/1 380... Discriminator Loss: 1.7437... Generator Loss: 0.4572
Epoch 1/1 390... Discriminator Loss: 1.4903... Generator Loss: 0.6395
Epoch 1/1 400... Discriminator Loss: 1.1567... Generator Loss: 0.8850
Epoch 1/1 410... Discriminator Loss: 1.8386... Generator Loss: 0.4633
Epoch 1/1 420... Discriminator Loss: 0.6093... Generator Loss: 1.6155
Epoch 1/1 430... Discriminator Loss: 1.6214... Generator Loss: 0.4922
Epoch 1/1 440... Discriminator Loss: 1.9284... Generator Loss: 0.3313
Epoch 1/1 450... Discriminator Loss: 0.9448... Generator Loss: 1.7820
Epoch 1/1 460... Discriminator Loss: 2.2392... Generator Loss: 0.2128
Epoch 1/1 470... Discriminator Loss: 1.3303... Generator Loss: 1.1700
Epoch 1/1 480... Discriminator Loss: 1.0697... Generator Loss: 1.5316
Epoch 1/1 490... Discriminator Loss: 1.2312... Generator Loss: 0.8369
Epoch 1/1 500... Discriminator Loss: 2.2376... Generator Loss: 0.2695
Epoch 1/1 510... Discriminator Loss: 1.3308... Generator Loss: 0.6409
Epoch 1/1 520... Discriminator Loss: 1.6682... Generator Loss: 0.5527
Epoch 1/1 530... Discriminator Loss: 1.7051... Generator Loss: 0.4788
Epoch 1/1 540... Discriminator Loss: 0.8699... Generator Loss: 0.9978
Epoch 1/1 550... Discriminator Loss: 1.2308... Generator Loss: 1.1526
Epoch 1/1 560... Discriminator Loss: 1.7339... Generator Loss: 0.3974
Epoch 1/1 570... Discriminator Loss: 1.3006... Generator Loss: 1.8408
Epoch 1/1 580... Discriminator Loss: 1.5141... Generator Loss: 0.6603
Epoch 1/1 590... Discriminator Loss: 0.7334... Generator Loss: 1.6333
Epoch 1/1 600... Discriminator Loss: 0.9685... Generator Loss: 1.0278
Epoch 1/1 610... Discriminator Loss: 1.1209... Generator Loss: 0.8103
Epoch 1/1 620... Discriminator Loss: 1.2350... Generator Loss: 0.6567
Epoch 1/1 630... Discriminator Loss: 0.7443... Generator Loss: 1.6287
Epoch 1/1 640... Discriminator Loss: 2.0994... Generator Loss: 0.4457
Epoch 1/1 650... Discriminator Loss: 1.4166... Generator Loss: 0.6758
Epoch 1/1 660... Discriminator Loss: 2.0071... Generator Loss: 0.6819
Epoch 1/1 670... Discriminator Loss: 0.8104... Generator Loss: 1.7407
Epoch 1/1 680... Discriminator Loss: 1.0667... Generator Loss: 0.7592
Epoch 1/1 690... Discriminator Loss: 1.2609... Generator Loss: 0.9398
Epoch 1/1 700... Discriminator Loss: 1.3778... Generator Loss: 0.8093
Epoch 1/1 710... Discriminator Loss: 1.1301... Generator Loss: 2.0657
Epoch 1/1 720... Discriminator Loss: 0.7265... Generator Loss: 1.9554
Epoch 1/1 730... Discriminator Loss: 0.6926... Generator Loss: 1.6002
Epoch 1/1 740... Discriminator Loss: 2.0265... Generator Loss: 0.4229
Epoch 1/1 750... Discriminator Loss: 1.0777... Generator Loss: 0.9334
Epoch 1/1 760... Discriminator Loss: 1.1116... Generator Loss: 0.7483
Epoch 1/1 770... Discriminator Loss: 1.0767... Generator Loss: 0.7097
Epoch 1/1 780... Discriminator Loss: 0.7803... Generator Loss: 1.6135
Epoch 1/1 790... Discriminator Loss: 1.8300... Generator Loss: 0.4110
Epoch 1/1 800... Discriminator Loss: 0.9231... Generator Loss: 1.3145
Epoch 1/1 810... Discriminator Loss: 1.0592... Generator Loss: 0.7826
Epoch 1/1 820... Discriminator Loss: 1.0491... Generator Loss: 1.0176
Epoch 1/1 830... Discriminator Loss: 0.5622... Generator Loss: 2.1354
Epoch 1/1 840... Discriminator Loss: 0.5692... Generator Loss: 1.6654
Epoch 1/1 850... Discriminator Loss: 0.8683... Generator Loss: 2.0414
Epoch 1/1 860... Discriminator Loss: 0.7628... Generator Loss: 1.2735
Epoch 1/1 870... Discriminator Loss: 1.6085... Generator Loss: 0.4428
Epoch 1/1 880... Discriminator Loss: 1.5387... Generator Loss: 2.3194
Epoch 1/1 890... Discriminator Loss: 1.4418... Generator Loss: 0.5101
Epoch 1/1 900... Discriminator Loss: 0.8959... Generator Loss: 1.0199
Epoch 1/1 910... Discriminator Loss: 1.2224... Generator Loss: 0.7767
Epoch 1/1 920... Discriminator Loss: 2.4812... Generator Loss: 0.2006
Epoch 1/1 930... Discriminator Loss: 1.1000... Generator Loss: 1.4781
Epoch 1/1 940... Discriminator Loss: 1.3863... Generator Loss: 0.5758
Epoch 1/1 950... Discriminator Loss: 1.2349... Generator Loss: 0.7064
Epoch 1/1 960... Discriminator Loss: 0.8433... Generator Loss: 1.1744
Epoch 1/1 970... Discriminator Loss: 1.7455... Generator Loss: 2.8387
Epoch 1/1 980... Discriminator Loss: 0.6958... Generator Loss: 1.7375
Epoch 1/1 990... Discriminator Loss: 0.9040... Generator Loss: 1.0587
Epoch 1/1 1000... Discriminator Loss: 1.6786... Generator Loss: 0.4530
Epoch 1/1 1010... Discriminator Loss: 0.8111... Generator Loss: 2.1592
Epoch 1/1 1020... Discriminator Loss: 1.5336... Generator Loss: 2.2079
Epoch 1/1 1030... Discriminator Loss: 1.2126... Generator Loss: 2.6361
Epoch 1/1 1040... Discriminator Loss: 1.7407... Generator Loss: 2.4077
Epoch 1/1 1050... Discriminator Loss: 0.8016... Generator Loss: 1.5869
Epoch 1/1 1060... Discriminator Loss: 1.5608... Generator Loss: 0.4663
Epoch 1/1 1070... Discriminator Loss: 0.8717... Generator Loss: 1.5165
Epoch 1/1 1080... Discriminator Loss: 0.8354... Generator Loss: 1.4717
Epoch 1/1 1090... Discriminator Loss: 1.0472... Generator Loss: 0.8088
Epoch 1/1 1100... Discriminator Loss: 0.9328... Generator Loss: 2.2946
Epoch 1/1 1110... Discriminator Loss: 1.2209... Generator Loss: 0.7522
Epoch 1/1 1120... Discriminator Loss: 0.8863... Generator Loss: 1.0571
Epoch 1/1 1130... Discriminator Loss: 1.3600... Generator Loss: 1.9793
Epoch 1/1 1140... Discriminator Loss: 1.1203... Generator Loss: 0.9159
Epoch 1/1 1150... Discriminator Loss: 1.1243... Generator Loss: 1.1193
Epoch 1/1 1160... Discriminator Loss: 1.0300... Generator Loss: 1.1195
Epoch 1/1 1170... Discriminator Loss: 1.2843... Generator Loss: 0.6744
Epoch 1/1 1180... Discriminator Loss: 1.1544... Generator Loss: 0.7738
Epoch 1/1 1190... Discriminator Loss: 1.2813... Generator Loss: 2.3786
Epoch 1/1 1200... Discriminator Loss: 1.4492... Generator Loss: 0.5331
Epoch 1/1 1210... Discriminator Loss: 0.9044... Generator Loss: 1.2350
Epoch 1/1 1220... Discriminator Loss: 1.2359... Generator Loss: 0.8784
Epoch 1/1 1230... Discriminator Loss: 1.3632... Generator Loss: 1.1432
Epoch 1/1 1240... Discriminator Loss: 1.4446... Generator Loss: 1.4959
Epoch 1/1 1250... Discriminator Loss: 0.8157... Generator Loss: 1.6561
Epoch 1/1 1260... Discriminator Loss: 1.2567... Generator Loss: 1.0764
Epoch 1/1 1270... Discriminator Loss: 1.2821... Generator Loss: 0.6264
Epoch 1/1 1280... Discriminator Loss: 1.0772... Generator Loss: 0.8812
Epoch 1/1 1290... Discriminator Loss: 1.3290... Generator Loss: 0.5811
Epoch 1/1 1300... Discriminator Loss: 0.9021... Generator Loss: 1.3475
Epoch 1/1 1310... Discriminator Loss: 1.2275... Generator Loss: 0.6577
Epoch 1/1 1320... Discriminator Loss: 1.0736... Generator Loss: 0.8713
Epoch 1/1 1330... Discriminator Loss: 0.9259... Generator Loss: 1.1513
Epoch 1/1 1340... Discriminator Loss: 0.9000... Generator Loss: 1.0548
Epoch 1/1 1350... Discriminator Loss: 1.0481... Generator Loss: 2.0788
Epoch 1/1 1360... Discriminator Loss: 1.2194... Generator Loss: 0.7312
Epoch 1/1 1370... Discriminator Loss: 0.9600... Generator Loss: 1.4536
Epoch 1/1 1380... Discriminator Loss: 0.9265... Generator Loss: 1.3775
Epoch 1/1 1390... Discriminator Loss: 0.8794... Generator Loss: 1.7396
Epoch 1/1 1400... Discriminator Loss: 1.0233... Generator Loss: 1.4709
Epoch 1/1 1410... Discriminator Loss: 1.3088... Generator Loss: 0.5528
Epoch 1/1 1420... Discriminator Loss: 1.6056... Generator Loss: 1.9774
Epoch 1/1 1430... Discriminator Loss: 0.9106... Generator Loss: 1.3606
Epoch 1/1 1440... Discriminator Loss: 1.0650... Generator Loss: 2.0017
Epoch 1/1 1450... Discriminator Loss: 1.1320... Generator Loss: 0.9095
Epoch 1/1 1460... Discriminator Loss: 1.2671... Generator Loss: 0.6910
Epoch 1/1 1470... Discriminator Loss: 1.0890... Generator Loss: 1.0459
Epoch 1/1 1480... Discriminator Loss: 1.3209... Generator Loss: 0.8116
Epoch 1/1 1490... Discriminator Loss: 1.1812... Generator Loss: 1.2747
Epoch 1/1 1500... Discriminator Loss: 1.1084... Generator Loss: 0.7795
Epoch 1/1 1510... Discriminator Loss: 1.2357... Generator Loss: 1.0824
Epoch 1/1 1520... Discriminator Loss: 1.0413... Generator Loss: 1.0316
Epoch 1/1 1530... Discriminator Loss: 1.3183... Generator Loss: 1.2918
Epoch 1/1 1540... Discriminator Loss: 1.2306... Generator Loss: 0.9491
Epoch 1/1 1550... Discriminator Loss: 1.0319... Generator Loss: 1.5595
Epoch 1/1 1560... Discriminator Loss: 1.2261... Generator Loss: 0.6597
Epoch 1/1 1570... Discriminator Loss: 1.2964... Generator Loss: 0.7948
Epoch 1/1 1580... Discriminator Loss: 1.4302... Generator Loss: 0.5060
Epoch 1/1 1590... Discriminator Loss: 0.9551... Generator Loss: 1.1198
Epoch 1/1 1600... Discriminator Loss: 1.1268... Generator Loss: 0.9043
Epoch 1/1 1610... Discriminator Loss: 1.2483... Generator Loss: 1.1213
Epoch 1/1 1620... Discriminator Loss: 1.2156... Generator Loss: 1.8068
Epoch 1/1 1630... Discriminator Loss: 1.0319... Generator Loss: 0.9955
Epoch 1/1 1640... Discriminator Loss: 0.9964... Generator Loss: 0.9586
Epoch 1/1 1650... Discriminator Loss: 1.0229... Generator Loss: 1.0736
Epoch 1/1 1660... Discriminator Loss: 1.2968... Generator Loss: 0.5544
Epoch 1/1 1670... Discriminator Loss: 1.1258... Generator Loss: 0.9579
Epoch 1/1 1680... Discriminator Loss: 1.1761... Generator Loss: 1.0731
Epoch 1/1 1690... Discriminator Loss: 1.2813... Generator Loss: 0.8203
Epoch 1/1 1700... Discriminator Loss: 1.0184... Generator Loss: 0.8936
Epoch 1/1 1710... Discriminator Loss: 1.0185... Generator Loss: 1.2037
Epoch 1/1 1720... Discriminator Loss: 1.1029... Generator Loss: 0.9988
Epoch 1/1 1730... Discriminator Loss: 0.9883... Generator Loss: 0.9699
Epoch 1/1 1740... Discriminator Loss: 1.3727... Generator Loss: 0.5830
Epoch 1/1 1750... Discriminator Loss: 1.2609... Generator Loss: 0.7592
Epoch 1/1 1760... Discriminator Loss: 1.4074... Generator Loss: 0.5322
Epoch 1/1 1770... Discriminator Loss: 1.0571... Generator Loss: 1.1208
Epoch 1/1 1780... Discriminator Loss: 1.0595... Generator Loss: 1.0573
Epoch 1/1 1790... Discriminator Loss: 1.1037... Generator Loss: 0.9013
Epoch 1/1 1800... Discriminator Loss: 1.0124... Generator Loss: 1.0781
Epoch 1/1 1810... Discriminator Loss: 1.0404... Generator Loss: 1.1957
Epoch 1/1 1820... Discriminator Loss: 0.9072... Generator Loss: 1.3233
Epoch 1/1 1830... Discriminator Loss: 1.3987... Generator Loss: 0.5956
Epoch 1/1 1840... Discriminator Loss: 1.0551... Generator Loss: 0.8540
Epoch 1/1 1850... Discriminator Loss: 1.0507... Generator Loss: 1.1905
Epoch 1/1 1860... Discriminator Loss: 0.7978... Generator Loss: 1.1782
Epoch 1/1 1870... Discriminator Loss: 1.0596... Generator Loss: 1.2033
Epoch 1/1 1880... Discriminator Loss: 1.0330... Generator Loss: 0.9033
Epoch 1/1 1890... Discriminator Loss: 1.2217... Generator Loss: 0.8961
Epoch 1/1 1900... Discriminator Loss: 1.1113... Generator Loss: 0.8139
Epoch 1/1 1910... Discriminator Loss: 1.1446... Generator Loss: 1.6165
Epoch 1/1 1920... Discriminator Loss: 1.0921... Generator Loss: 1.1604
Epoch 1/1 1930... Discriminator Loss: 1.2727... Generator Loss: 0.8232
Epoch 1/1 1940... Discriminator Loss: 0.9849... Generator Loss: 1.3283
Epoch 1/1 1950... Discriminator Loss: 1.1097... Generator Loss: 0.9721
Epoch 1/1 1960... Discriminator Loss: 1.2295... Generator Loss: 0.9212
Epoch 1/1 1970... Discriminator Loss: 1.3312... Generator Loss: 0.7284
Epoch 1/1 1980... Discriminator Loss: 1.1012... Generator Loss: 0.8893
Epoch 1/1 1990... Discriminator Loss: 1.4719... Generator Loss: 0.5158
Epoch 1/1 2000... Discriminator Loss: 0.8020... Generator Loss: 1.5235
Epoch 1/1 2010... Discriminator Loss: 1.1954... Generator Loss: 0.9270
Epoch 1/1 2020... Discriminator Loss: 1.2011... Generator Loss: 0.8179
Epoch 1/1 2030... Discriminator Loss: 1.5732... Generator Loss: 0.6750
Epoch 1/1 2040... Discriminator Loss: 1.1171... Generator Loss: 1.0405
Epoch 1/1 2050... Discriminator Loss: 1.2755... Generator Loss: 0.7504
Epoch 1/1 2060... Discriminator Loss: 1.2552... Generator Loss: 0.8459
Epoch 1/1 2070... Discriminator Loss: 1.3339... Generator Loss: 0.7175
Epoch 1/1 2080... Discriminator Loss: 1.0260... Generator Loss: 1.0243
Epoch 1/1 2090... Discriminator Loss: 1.0456... Generator Loss: 1.1711
Epoch 1/1 2100... Discriminator Loss: 1.1604... Generator Loss: 0.8226
Epoch 1/1 2110... Discriminator Loss: 1.1245... Generator Loss: 0.9043
Epoch 1/1 2120... Discriminator Loss: 0.7162... Generator Loss: 1.5368
Epoch 1/1 2130... Discriminator Loss: 1.2255... Generator Loss: 1.1913
Epoch 1/1 2140... Discriminator Loss: 1.2054... Generator Loss: 0.8668
Epoch 1/1 2150... Discriminator Loss: 1.1099... Generator Loss: 1.0665
Epoch 1/1 2160... Discriminator Loss: 1.0353... Generator Loss: 0.9332
Epoch 1/1 2170... Discriminator Loss: 1.3401... Generator Loss: 0.7556
Epoch 1/1 2180... Discriminator Loss: 1.0202... Generator Loss: 1.0470
Epoch 1/1 2190... Discriminator Loss: 0.8031... Generator Loss: 1.3280
Epoch 1/1 2200... Discriminator Loss: 1.1587... Generator Loss: 0.8255
Epoch 1/1 2210... Discriminator Loss: 1.2540... Generator Loss: 0.7074
Epoch 1/1 2220... Discriminator Loss: 1.1895... Generator Loss: 1.0878
Epoch 1/1 2230... Discriminator Loss: 1.0281... Generator Loss: 1.1305
Epoch 1/1 2240... Discriminator Loss: 1.0829... Generator Loss: 0.9868
Epoch 1/1 2250... Discriminator Loss: 1.0246... Generator Loss: 0.9176
Epoch 1/1 2260... Discriminator Loss: 1.0705... Generator Loss: 0.9702
Epoch 1/1 2270... Discriminator Loss: 1.3245... Generator Loss: 0.8481
Epoch 1/1 2280... Discriminator Loss: 0.9542... Generator Loss: 1.2211
Epoch 1/1 2290... Discriminator Loss: 1.1479... Generator Loss: 0.8880
Epoch 1/1 2300... Discriminator Loss: 1.0079... Generator Loss: 1.0235
Epoch 1/1 2310... Discriminator Loss: 1.0900... Generator Loss: 1.0359
Epoch 1/1 2320... Discriminator Loss: 1.4476... Generator Loss: 0.7779
Epoch 1/1 2330... Discriminator Loss: 0.9591... Generator Loss: 1.0955
Epoch 1/1 2340... Discriminator Loss: 1.2216... Generator Loss: 0.8018
Epoch 1/1 2350... Discriminator Loss: 1.1136... Generator Loss: 0.8827
Epoch 1/1 2360... Discriminator Loss: 1.2416... Generator Loss: 0.7466
Epoch 1/1 2370... Discriminator Loss: 1.0880... Generator Loss: 0.7904
Epoch 1/1 2380... Discriminator Loss: 0.9117... Generator Loss: 1.1563
Epoch 1/1 2390... Discriminator Loss: 0.9671... Generator Loss: 0.8712
Epoch 1/1 2400... Discriminator Loss: 1.0001... Generator Loss: 0.8863
Epoch 1/1 2410... Discriminator Loss: 0.9382... Generator Loss: 1.1408
Epoch 1/1 2420... Discriminator Loss: 1.2725... Generator Loss: 1.3620
Epoch 1/1 2430... Discriminator Loss: 1.1085... Generator Loss: 1.3113
Epoch 1/1 2440... Discriminator Loss: 0.9331... Generator Loss: 0.9487
Epoch 1/1 2450... Discriminator Loss: 1.1232... Generator Loss: 1.0708
Epoch 1/1 2460... Discriminator Loss: 1.4902... Generator Loss: 0.4610
Epoch 1/1 2470... Discriminator Loss: 0.8396... Generator Loss: 1.6691
Epoch 1/1 2480... Discriminator Loss: 1.0743... Generator Loss: 0.8932
Epoch 1/1 2490... Discriminator Loss: 0.9600... Generator Loss: 0.9474
Epoch 1/1 2500... Discriminator Loss: 1.2517... Generator Loss: 0.8785
Epoch 1/1 2510... Discriminator Loss: 1.2214... Generator Loss: 0.5773
Epoch 1/1 2520... Discriminator Loss: 0.9155... Generator Loss: 0.9548
Epoch 1/1 2530... Discriminator Loss: 1.0554... Generator Loss: 0.8819
Epoch 1/1 2540... Discriminator Loss: 1.0147... Generator Loss: 1.0525
Epoch 1/1 2550... Discriminator Loss: 1.1681... Generator Loss: 0.8230
Epoch 1/1 2560... Discriminator Loss: 1.1986... Generator Loss: 0.8542
Epoch 1/1 2570... Discriminator Loss: 1.0213... Generator Loss: 1.0398
Epoch 1/1 2580... Discriminator Loss: 1.1210... Generator Loss: 1.0082
Epoch 1/1 2590... Discriminator Loss: 1.0738... Generator Loss: 0.8429
Epoch 1/1 2600... Discriminator Loss: 2.1166... Generator Loss: 1.6147
Epoch 1/1 2610... Discriminator Loss: 1.1058... Generator Loss: 0.9866
Epoch 1/1 2620... Discriminator Loss: 1.4888... Generator Loss: 0.5594
Epoch 1/1 2630... Discriminator Loss: 1.0012... Generator Loss: 1.0634
Epoch 1/1 2640... Discriminator Loss: 1.1532... Generator Loss: 0.8237
Epoch 1/1 2650... Discriminator Loss: 1.2550... Generator Loss: 0.6361
Epoch 1/1 2660... Discriminator Loss: 1.1126... Generator Loss: 1.0425
Epoch 1/1 2670... Discriminator Loss: 1.0442... Generator Loss: 0.9728
Epoch 1/1 2680... Discriminator Loss: 1.2091... Generator Loss: 0.9300
Epoch 1/1 2690... Discriminator Loss: 0.9688... Generator Loss: 1.2047
Epoch 1/1 2700... Discriminator Loss: 0.9814... Generator Loss: 1.2524
Epoch 1/1 2710... Discriminator Loss: 0.9687... Generator Loss: 1.1516
Epoch 1/1 2720... Discriminator Loss: 1.3276... Generator Loss: 0.7137
Epoch 1/1 2730... Discriminator Loss: 1.0956... Generator Loss: 0.8412
Epoch 1/1 2740... Discriminator Loss: 1.4306... Generator Loss: 0.4843
Epoch 1/1 2750... Discriminator Loss: 1.2545... Generator Loss: 1.0252
Epoch 1/1 2760... Discriminator Loss: 1.4500... Generator Loss: 0.4898
Epoch 1/1 2770... Discriminator Loss: 1.2860... Generator Loss: 1.5218
Epoch 1/1 2780... Discriminator Loss: 1.2819... Generator Loss: 0.5708
Epoch 1/1 2790... Discriminator Loss: 0.8926... Generator Loss: 1.3451
Epoch 1/1 2800... Discriminator Loss: 1.0887... Generator Loss: 1.0790
Epoch 1/1 2810... Discriminator Loss: 0.9553... Generator Loss: 1.0632
Epoch 1/1 2820... Discriminator Loss: 0.8072... Generator Loss: 1.0688
Epoch 1/1 2830... Discriminator Loss: 1.2968... Generator Loss: 0.6639
Epoch 1/1 2840... Discriminator Loss: 0.9719... Generator Loss: 1.2996
Epoch 1/1 2850... Discriminator Loss: 0.8141... Generator Loss: 1.6354
Epoch 1/1 2860... Discriminator Loss: 1.2206... Generator Loss: 0.7992
Epoch 1/1 2870... Discriminator Loss: 1.3040... Generator Loss: 0.5385
Epoch 1/1 2880... Discriminator Loss: 1.1169... Generator Loss: 0.8614
Epoch 1/1 2890... Discriminator Loss: 1.0798... Generator Loss: 0.8220
Epoch 1/1 2900... Discriminator Loss: 1.1270... Generator Loss: 0.7710
Epoch 1/1 2910... Discriminator Loss: 1.2057... Generator Loss: 0.6158
Epoch 1/1 2920... Discriminator Loss: 1.1224... Generator Loss: 0.9018
Epoch 1/1 2930... Discriminator Loss: 0.9614... Generator Loss: 1.1404
Epoch 1/1 2940... Discriminator Loss: 1.0775... Generator Loss: 1.0552
Epoch 1/1 2950... Discriminator Loss: 0.9864... Generator Loss: 1.0781
Epoch 1/1 2960... Discriminator Loss: 1.1563... Generator Loss: 0.7243
Epoch 1/1 2970... Discriminator Loss: 0.9365... Generator Loss: 1.1046
Epoch 1/1 2980... Discriminator Loss: 1.3122... Generator Loss: 0.5645
Epoch 1/1 2990... Discriminator Loss: 1.2749... Generator Loss: 0.6316
Epoch 1/1 3000... Discriminator Loss: 1.1850... Generator Loss: 0.9261
Epoch 1/1 3010... Discriminator Loss: 1.0210... Generator Loss: 1.2168
Epoch 1/1 3020... Discriminator Loss: 1.2403... Generator Loss: 0.6373
Epoch 1/1 3030... Discriminator Loss: 1.2645... Generator Loss: 1.3456
Epoch 1/1 3040... Discriminator Loss: 0.7777... Generator Loss: 1.3915
Epoch 1/1 3050... Discriminator Loss: 1.0881... Generator Loss: 1.0644
Epoch 1/1 3060... Discriminator Loss: 1.0334... Generator Loss: 1.0459
Epoch 1/1 3070... Discriminator Loss: 0.9181... Generator Loss: 0.9580
Epoch 1/1 3080... Discriminator Loss: 1.0662... Generator Loss: 0.8269
Epoch 1/1 3090... Discriminator Loss: 0.9662... Generator Loss: 1.5117
Epoch 1/1 3100... Discriminator Loss: 0.9553... Generator Loss: 1.3497
Epoch 1/1 3110... Discriminator Loss: 1.2169... Generator Loss: 0.8358
Epoch 1/1 3120... Discriminator Loss: 1.0734... Generator Loss: 1.1890
Epoch 1/1 3130... Discriminator Loss: 0.8214... Generator Loss: 1.7209
Epoch 1/1 3140... Discriminator Loss: 1.2014... Generator Loss: 0.7802
Epoch 1/1 3150... Discriminator Loss: 1.2245... Generator Loss: 0.6785
Epoch 1/1 3160... Discriminator Loss: 0.8449... Generator Loss: 1.1579
Epoch 1/1 3170... Discriminator Loss: 1.3415... Generator Loss: 1.0600
Epoch 1/1 3180... Discriminator Loss: 1.1457... Generator Loss: 0.8062
Epoch 1/1 3190... Discriminator Loss: 1.1478... Generator Loss: 0.7430
Epoch 1/1 3200... Discriminator Loss: 0.9431... Generator Loss: 0.8978
Epoch 1/1 3210... Discriminator Loss: 0.8585... Generator Loss: 1.3023
Epoch 1/1 3220... Discriminator Loss: 0.8220... Generator Loss: 1.3994
Epoch 1/1 3230... Discriminator Loss: 1.1895... Generator Loss: 1.3197
Epoch 1/1 3240... Discriminator Loss: 0.9630... Generator Loss: 1.0560
Epoch 1/1 3250... Discriminator Loss: 1.1002... Generator Loss: 1.3742
Epoch 1/1 3260... Discriminator Loss: 1.0743... Generator Loss: 1.1904
Epoch 1/1 3270... Discriminator Loss: 1.0052... Generator Loss: 1.2374
Epoch 1/1 3280... Discriminator Loss: 0.8637... Generator Loss: 1.3258
Epoch 1/1 3290... Discriminator Loss: 0.6474... Generator Loss: 1.6269
Epoch 1/1 3300... Discriminator Loss: 1.1422... Generator Loss: 0.6741
Epoch 1/1 3310... Discriminator Loss: 1.1549... Generator Loss: 0.8771
Epoch 1/1 3320... Discriminator Loss: 1.1486... Generator Loss: 1.2285
Epoch 1/1 3330... Discriminator Loss: 0.7329... Generator Loss: 1.3605
Epoch 1/1 3340... Discriminator Loss: 1.1454... Generator Loss: 0.7406
Epoch 1/1 3350... Discriminator Loss: 0.9968... Generator Loss: 0.8729
Epoch 1/1 3360... Discriminator Loss: 0.8411... Generator Loss: 1.1006
Epoch 1/1 3370... Discriminator Loss: 0.9790... Generator Loss: 0.8998
Epoch 1/1 3380... Discriminator Loss: 1.0040... Generator Loss: 0.8355
Epoch 1/1 3390... Discriminator Loss: 1.2434... Generator Loss: 0.8491
Epoch 1/1 3400... Discriminator Loss: 1.1372... Generator Loss: 0.8048
Epoch 1/1 3410... Discriminator Loss: 0.9779... Generator Loss: 1.0192
Epoch 1/1 3420... Discriminator Loss: 0.9991... Generator Loss: 1.1779
Epoch 1/1 3430... Discriminator Loss: 1.0964... Generator Loss: 0.8751
Epoch 1/1 3440... Discriminator Loss: 0.9081... Generator Loss: 1.2451
Epoch 1/1 3450... Discriminator Loss: 1.0890... Generator Loss: 0.8630
Epoch 1/1 3460... Discriminator Loss: 1.1083... Generator Loss: 2.1205
Epoch 1/1 3470... Discriminator Loss: 1.0159... Generator Loss: 0.8986
Epoch 1/1 3480... Discriminator Loss: 0.9712... Generator Loss: 1.3646
Epoch 1/1 3490... Discriminator Loss: 1.0775... Generator Loss: 0.9883
Epoch 1/1 3500... Discriminator Loss: 1.1156... Generator Loss: 1.0005
Epoch 1/1 3510... Discriminator Loss: 1.2644... Generator Loss: 1.3754
Epoch 1/1 3520... Discriminator Loss: 1.2289... Generator Loss: 0.6145
Epoch 1/1 3530... Discriminator Loss: 1.1061... Generator Loss: 0.8574
Epoch 1/1 3540... Discriminator Loss: 0.9191... Generator Loss: 1.0629
Epoch 1/1 3550... Discriminator Loss: 0.8697... Generator Loss: 1.3558
Epoch 1/1 3560... Discriminator Loss: 1.2650... Generator Loss: 0.6693
Epoch 1/1 3570... Discriminator Loss: 1.0240... Generator Loss: 0.9793
Epoch 1/1 3580... Discriminator Loss: 1.0608... Generator Loss: 0.8594
Epoch 1/1 3590... Discriminator Loss: 1.0076... Generator Loss: 1.8062
Epoch 1/1 3600... Discriminator Loss: 1.1812... Generator Loss: 0.6653
Epoch 1/1 3610... Discriminator Loss: 1.3271... Generator Loss: 0.5618
Epoch 1/1 3620... Discriminator Loss: 0.9989... Generator Loss: 1.1504
Epoch 1/1 3630... Discriminator Loss: 0.9524... Generator Loss: 1.1693
Epoch 1/1 3640... Discriminator Loss: 1.1655... Generator Loss: 0.9051
Epoch 1/1 3650... Discriminator Loss: 1.0762... Generator Loss: 0.8686
Epoch 1/1 3660... Discriminator Loss: 1.1694... Generator Loss: 0.8902
Epoch 1/1 3670... Discriminator Loss: 1.0304... Generator Loss: 0.9922
Epoch 1/1 3680... Discriminator Loss: 0.9235... Generator Loss: 1.1500
Epoch 1/1 3690... Discriminator Loss: 1.1569... Generator Loss: 1.0038
Epoch 1/1 3700... Discriminator Loss: 0.9328... Generator Loss: 0.9595
Epoch 1/1 3710... Discriminator Loss: 1.2828... Generator Loss: 0.7063
Epoch 1/1 3720... Discriminator Loss: 0.8719... Generator Loss: 1.2880
Epoch 1/1 3730... Discriminator Loss: 0.9994... Generator Loss: 1.0282
Epoch 1/1 3740... Discriminator Loss: 1.2108... Generator Loss: 0.8316
Epoch 1/1 3750... Discriminator Loss: 0.9562... Generator Loss: 1.2562
Epoch 1/1 3760... Discriminator Loss: 0.8489... Generator Loss: 1.3267
Epoch 1/1 3770... Discriminator Loss: 1.2697... Generator Loss: 0.8451
Epoch 1/1 3780... Discriminator Loss: 1.0683... Generator Loss: 0.9080
Epoch 1/1 3790... Discriminator Loss: 0.9339... Generator Loss: 0.9477
Epoch 1/1 3800... Discriminator Loss: 1.3236... Generator Loss: 0.8115
Epoch 1/1 3810... Discriminator Loss: 1.0161... Generator Loss: 1.2256
Epoch 1/1 3820... Discriminator Loss: 0.9797... Generator Loss: 1.3303
Epoch 1/1 3830... Discriminator Loss: 1.1531... Generator Loss: 0.6257
Epoch 1/1 3840... Discriminator Loss: 0.7453... Generator Loss: 1.5367
Epoch 1/1 3850... Discriminator Loss: 0.8656... Generator Loss: 1.9927
Epoch 1/1 3860... Discriminator Loss: 1.3037... Generator Loss: 1.0789
Epoch 1/1 3870... Discriminator Loss: 0.9577... Generator Loss: 1.0617
Epoch 1/1 3880... Discriminator Loss: 1.1694... Generator Loss: 0.9233
Epoch 1/1 3890... Discriminator Loss: 1.0987... Generator Loss: 1.1616
Epoch 1/1 3900... Discriminator Loss: 1.4000... Generator Loss: 0.6534
Epoch 1/1 3910... Discriminator Loss: 0.9737... Generator Loss: 1.3132
Epoch 1/1 3920... Discriminator Loss: 1.3069... Generator Loss: 0.9281
Epoch 1/1 3930... Discriminator Loss: 0.8951... Generator Loss: 0.9276
Epoch 1/1 3940... Discriminator Loss: 1.0650... Generator Loss: 1.0587
Epoch 1/1 3950... Discriminator Loss: 0.9577... Generator Loss: 0.8260
Epoch 1/1 3960... Discriminator Loss: 1.0620... Generator Loss: 1.7250
Epoch 1/1 3970... Discriminator Loss: 1.0400... Generator Loss: 0.8921
Epoch 1/1 3980... Discriminator Loss: 0.9897... Generator Loss: 0.9206
Epoch 1/1 3990... Discriminator Loss: 0.8672... Generator Loss: 1.2974
Epoch 1/1 4000... Discriminator Loss: 1.2762... Generator Loss: 0.6675
Epoch 1/1 4010... Discriminator Loss: 0.7091... Generator Loss: 1.2485
Epoch 1/1 4020... Discriminator Loss: 1.9266... Generator Loss: 3.1936
Epoch 1/1 4030... Discriminator Loss: 0.9591... Generator Loss: 1.2643
Epoch 1/1 4040... Discriminator Loss: 0.9862... Generator Loss: 1.3045
Epoch 1/1 4050... Discriminator Loss: 1.6579... Generator Loss: 0.3820
Epoch 1/1 4060... Discriminator Loss: 0.9041... Generator Loss: 1.3259
Epoch 1/1 4070... Discriminator Loss: 0.9097... Generator Loss: 1.2898
Epoch 1/1 4080... Discriminator Loss: 1.1185... Generator Loss: 0.8009
Epoch 1/1 4090... Discriminator Loss: 1.3118... Generator Loss: 1.0533
Epoch 1/1 4100... Discriminator Loss: 1.7692... Generator Loss: 0.5032
Epoch 1/1 4110... Discriminator Loss: 1.4969... Generator Loss: 0.5775
Epoch 1/1 4120... Discriminator Loss: 0.7864... Generator Loss: 1.9270
Epoch 1/1 4130... Discriminator Loss: 0.7613... Generator Loss: 1.7827
Epoch 1/1... Discriminator Loss: 0.9720... Generator Loss: 1.0641

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.